SavedAdd your journal

Journals / International Journal of Aeronautics and Astronautics / 2026 / Cilt: 7 Sayı: 1

The shift from airline efficiency to system constraints: A system-level analysis of U.S. Airline operational performance (2022-2024)

Havayolu verimliliğinden sistem kısıtlamalarına geçiş: ABD Havayollarının operasyonel performansına ilişkin sistem düzeyinde bir analiz (2022-2024)

Ahmet Ertek(KTO KARATAY ÜNİVERSİTESİ, UYGULAMALI BİLİMLER YÜKSEKOKULU, PİLOTAJ BÖLÜMÜ)0000-0002-8156-5075
Pages
16–31
Publication Date
2026-06-30
🏛️Bibliographic metadata and full text access for this article are provided via DergiPark.
DergiPark Record ↗
✨

AI Summary & Key Takeaways

AI Insight
⚡ TL;DR(Too Long; Didn't Read)

Average on-time rates improved from 76.3% in 2022 to 78.3% in 2023, before slightly declining to 78.0% in 2024, with 2023 showing the best overall performance.

📌 3 Key Takeaways

🎯
Objective & Hypothesis:Airline operational performance is substantially affected by delays and cancellations.
📊
Key Findings & Data:This study presents a comparative analysis of performance changes between 2022 and 2024 using key indicators such as on-time performance, cancellations, diversions, and delay causes (air carrier, weather, security, NAS, and late aircraft).
💡
Conclusion & Impact:Average on-time rates improved from 76.3% in 2022 to 78.3% in 2023, before slightly declining to 78.0% in 2024, with 2023 showing the best overall performance.
🔬Design:Araştırma Makalesi
📍Scope:2022 Verileri
⚙️Variables:Gecikmelerin yayılması · Hava taşımacılığı sistemi · Havayolu işletme performansı

Abstract

Airline operational performance is substantially affected by delays and cancellations. This study presents a comparative analysis of performance changes between 2022 and 2024 using key indicators such as on-time performance, cancellations, diversions, and delay causes (air carrier, weather, security, NAS, and late aircraft). The results reveal a clear seasonality pattern across all three years. Average on-time rates improved from 76.3% in 2022 to 78.3% in 2023, before slightly declining to 78.0% in 2024, with 2023 showing the best overall performance. Peak on-time performance occurred in October 2022 (83.25%), November 2023 (86.22%), and October 2024 (85.81%), while the lowest levels were observed in December 2022 (68.72%) and July in both 2023 (69.45%) and 2024 (68.08%). Cancellation rates were highest in 2022 at approximately 2.8%, followed by a notable improvement in 2023 (1.3%) and stability in 2024 (1.35%). Diversion rates remained consistently low (around 0.23%-0.24%) with no significant variation. In terms of delay components, Air Carrier Delays decreased over time, whereas NAS Delays and Late Arriving Aircraft Delays showed a gradual increase. Overall, the findings suggest the possibility of a gradual shift from operational pressures associated with Air Carrier Delays toward broader system-level constraints, particularly NAS-related and Late Arriving Aircraft delays, affecting operational performance.

Özet

Havayolu işletmelerinin operasyonel performansı gecikmelerden ve iptallerden önemli ölçüde etkilenmektedir. Bu çalışma, zamanında performans, iptaller, sapmalar ve gecikme nedenleri (hava taşıyıcısı, hava durumu, güvenlik, NAS ve geciken uçak) gibi temel göstergeleri kullanarak 2022 ile 2024 arasındaki performans değişikliklerinin karşılaştırmalı bir analizini sunmaktadır. Sonuçlar, üç yıl boyunca net bir mevsimsellik modelini ortaya koymaktadır. Ortalama zamanında performans oranları 2022 yılındaki %76,3'ten 2023'te %78,3'e yükselmiştir, ardından 2024'te hafif bir düşüşle %78,0'a gerilemiştir ve 2023 en iyi genel performansı göstermektedir. Zamanında en yüksek performans Ekim 2022 (%83,25), Kasım 2023 (%86,22) ve Ekim 2024'te (%85,81) görülürken, en düşük seviyeler Aralık 2022 (%68,72) ve Temmuz aylarında hem 2023 (%69,45) hem de 2024 (%68,08) yıllarında gözlenmektedir. İptal oranları yaklaşık %2,8 ile 2022'de en yüksek seviyedeyken; bunu 2023'te kayda değer bir iyileşme (%1,3) ve 2024'te (%1,35) istikrar izlemektedir. Yönlendirme oranları, önemli bir değişiklik olmaksızın sürekli olarak düşük (%0,23-%0,24 civarında) kalmıştır. Gecikme bileşenleri açısından hava taşıyıcısı gecikmeleri zamanla azalırken, NAS gecikmeleri ve geç gelen uçak gecikmeleri kademeli bir artış göstermektedir. Genel olarak bulgular, hava taşıyıcısı gecikmeleriyle ilişkili operasyonel baskılardan, operasyonel performansı etkileyen, özellikle NAS ile ilgili ve geç gelen uçak gecikmeleri gibi daha geniş sistem düzeyindeki kısıtlamalara doğru kademeli bir geçiş olasılığını ortaya koymaktadır.

🔖

Cite This Article

Ertek, A. (2026). The shift from airline efficiency to system constraints: A system-level analysis of U.S. Airline operational performance (2022-2024). International Journal of Aeronautics and Astronautics, Cilt: 7 Sayı: 1, 16–31. https://doi.org/10.55212/ijaa.1945545

📄 Full Text PDF Viewer

📄

PDF görüntüleyici hazırlanıyor...

References (42)

  1. Addu, S., Ambati, P. R., Kondakalla, S. R., Kunchakuri, H., & Thottempudi, M. (2022). Predicting delay in flights using machine learning. In Proceedings of the 2022 International Conference on Applied Artificial Intelligence and Computing (ICAAIC) (pp. 374-379). Salem, India.
  2. Al-Sultan, A. T. (2016). Optimization of airport check-in scheduling at passenger terminal. International Journal of Applied Business and Economic Research, 14, 3233-3245.
  3. Arıkan, M., Deshpande, V., & Sohoni, M. (2013). Building reliable air-travel infrastructure using empirical data and stochastic models of airline networks. Operations Research, 61, 45-64. DOI: 10.1287/opre.1120.1146.
  4. Arıkan, U., Gürel, S., & Aktürk, M. S. (2017). Flight network-based approach for integrated airline recovery with cruise speed control. Transportation Science, 51(4), 1259-1287. https://doi.org/10.1287/trsc.2016.0716.
  5. Barros, C. P., & Peypoch, N. (2009). An evaluation of European airlines’ operational performance. International Journal of Production Economics, 122(2), 525-533. https://doi.org/10.1016/j.ijpe.2009.04.016.
  6. Belkoura, S., Peña, J. M., & Zanin, M. (2016). Generation and recovery of airborne delays in air transport. Transportation Research Part C: Emerging Technologies, 69, 436-450.
  7. Bratu, S., & Barnhart, C. (2006). Flight operations recovery: New approaches considering passenger recovery. Journal of Scheduling, 9, 279-298. https://doi.org/10.1007/s10951-006-6781-0
  8. Brueckner, J. K., & Zhang, Y. (2001). A model of scheduling in airline networks: How a hub-and-spoke system affects flight frequency, fares and welfare. Journal of Transport Economics and Policy, 35(2), 195-222. DOI: 10.3828/jtep.2001.35.2.195.
  9. Brueckner, J. K., Czerny, A. I., & Gaggero, A. A. (2021). Airline mitigation of propagated delays via schedule buffers: Theory and empirics. Transportation Research Part E: Logistics and Transportation Review, 150, Article 102333. https://doi.org/10.1016/j.tre.2021.102333.
  10. Brueckner, J. K., Czerny, A. I., & Gaggero, A. A. (2022). Airline delay propagation: A simple method for measuring its extent and determinants. Transportation Research Part B: Methodological, 162, 55-71. https://doi.org/10.1016/j.trb.2022.05.003.
  11. Campanelli, B., Fleurquin, P., Eguíluz, V. M., Ramasco, J. J., Arranz, A., Extebarria, I., & Ciruelos, C. (2014). Modeling reactionary delays in the European air transport network. In Proceedings of the Fourth SESAR Innovation Days. Madrid, Spain.
  12. Castro, A., Rocha, A. P., & Oliveira, E. (2014). A new approach for disruption management in airline operations control (Vol. 562). Studies in Computational Intelligence. https://doi.org/10.1007/978-3-662-43373-7
  13. Evans, A., & Schäfer, A. W. (2014). Simulating airline operational responses to airport capacity constraints. Transport Policy, 34, 5-13. DOI: 10.1016/j.tranpol.2014.02.013.
  14. Gillen, D., Jacquillat, A., & Odoni, A. R. (2016). Airport demand management: The operations research and economics perspectives and potential synergies. Transportation Research Part A: Policy and Practice, 94, 495-513. DOI: 10.1016/j.tra.2016.10.011.
  15. Gładys, S., Kwasiborska, A., & Postół, J. (2022). Determination of the impact of disruptions in ground handling on aircraft fuel consumption. Transport Problems, 17, 115-126. DOI: 10.20858/tp.2022.17.2.10.
  16. Gudiel Pineda, P. J., Liou, J. J. H., Hsu, C.-C., & Chuang, Y.-C. (2018). An integrated MCDM model for improving airline operational and financial performance. Journal of Air Transport Management, 68, 103-117. https://doi.org/10.1016/j.jairtraman.2017.06.003.
  17. Hajko, J., & Badánik, B. (2020). Airline On-Time Performance Management. Transportation Research Procedia, 51, 82-97. https://doi.org/10.1016/j.trpro.2020.11.011.
  18. Hao, L., Hansen, M., Zhang, Y., & Post, J. (2014). New York, New York: Two ways of estimating the delay impact of New York airports. Transportation Research Part E: Logistics and Transportation Review, 70, 245-260. DOI: 10.1016/j.tre.2014.07.004.
  19. Hsu, C.-I., Chao, C.-C., & Hsu, N.-W. (2015). Control strategies for departure process delays at airport passenger terminals. Transport Planning and Technology, 38, 214-237. DOI: 10.1080/03081060.2014.959358.
  20. Ionescu, L., Gwiggner, C., & Kliewer, N. (2016). Data analysis of delays in airline networks. Business & Information Systems Engineering, 58, 119-133. DOI: 10.1007/s12599-015-0391-3.
  21. Kafle, N., & Zou, B. (2016). Modeling flight delay propagation: A new analytical-econometric approach. Transportation Research Part B: Methodological, 93, 42-52. DOI: 10.1016/j.trb.2016.08.012.
  22. Kohl, N., Larsen, A., Larsen, J., Ross, A., & Tiourine, S. (2007). Airline disruption management-Perspectives, experiences and outlook. Journal of Air Transport Management, 13(3), 149-162. https://doi.org/10.1016/j.jairtraman.2007.01.001.
  23. Lin, Y., Li, L., Ren, P., Wang, Y., & Szeto, W. Y. (2021). From aircraft tracking data to network delay model: A data-driven approach considering en-route congestion. Transportation Research Part C: Emerging Technologies, 131, 103329. https://doi.org/10.1016/j.trc.2021.103329.
  24. Liu, W., Zhao, Q., & Delahaye, D. (2022). Research on slot allocation for airport network in the presence of uncertainty. Journal of Air Transport Management, 104, 102269. DOI: 10.1016/j.jairtraman.2022.102269.
  25. Pan, W., Wang, Z., Zhou, G., Pan, Z., & Zhang, H. (2015). Flight delay cost optimization based on ground power supply. In Proceedings of the 2015 International Industrial Informatics and Computer Engineering Conference. DOI 10.2991/iiicec-15.2015.77.
  26. Petersen, J. D. (2012). Large-scale mixed integer optimization approaches for scheduling airline operations under irregularity. Georgia Institute of Technology.
  27. Rebollo, J. J., & Balakrishnan, H. (2014). Characterization and prediction of air traffic delays. Transportation Research Part C: Emerging Technologies, 44, 231-241. DOI: 10.1016/j.trc.2014.04.007.
  28. Ribeiro, N. A., Tay, J., Ng, W., & Birolini, S. (2025). Delay predictive analytics for airport capacity management. Transportation Research Part C: Emerging Technologies, 171, 104947.https://doi.org/10.1016/j.trc.2024.104947.
  29. Ryerson, M. S., Hansen, M., & Bonn, J. (2014). Time to burn: Flight delay, terminal efficiency, and fuel consumption in the National Airspace System. Transportation Research Part A: Policy and Practice, 69, 286-298. https://doi.org/10.1016/j.tra.2014.08.024.
  30. Skorupski, J., & Wierzbinska, M. (2015). A method to evaluate the time of waiting for a late passenger. Journal of Air Transport Management, 47, 79-89. https://doi.org/10.1016/j.jairtraman.2015.05.001.
  31. Takeichi, N. (2017). Nominal flight time optimization for arrival time scheduling through estimation/resolution of delay accumulation. Transportation Research Part C: Emerging Technologies, 77, 433-443. DOI: 10.1016/j.trc.2017.01.025.
  32. U.S. Department of Transportation. (2025). Air travel consumer reports. https://www.transportation.gov/individuals/aviation-consumer-protection/air-travel-consumer-reports (Erişim tarihi: 20.11.2025).
  33. Wang, C., Hu, M., Yang, L., & Zhao, Z. (2021). Prediction of air traffic delays: An agent-based model introducing refined parameter estimation methods. PLOS ONE, 16(4), e0249754. https://doi.org/10.1371/journal.pone.0249754.
  34. Wu, C.L. (2005). Inherent delays and operational reliability of airline schedules. Journal of Air Transport Management, 11(4), 273-282. https://doi.org/10.1016/j.jairtraman.2005.01.005.
  35. Wu, C.-L., & Truong, T. (2014). Improving the IATA delay data coding system for enhanced data analytics. Journal of Air Transport Management, 40, 78-85. DOI:10.1016/j.jairtraman.2014.06.001.
  36. Xing, Z. W., Yu, J., & Lu, H. Y. (2014). Optimal control of flight delays allocation at airport. Applied Mechanics and Materials, 513-517, 4494-4498. DOI: 10.4028/www.scientific.net/AMM.513-517.4494.
  37. Yıldız, S., Aydemir, O., Memiş, A., & Varlı, S. (2022). A turnaround control system to automatically detect and monitor the time stamps of ground service actions in airports: A deep learning and computer vision based approach. Engineering Applications of Artificial Intelligence, 114, 105032. DOI: 10.1016/j.engappai.2022.105032.
  38. Zámková, M. & Prokop, M. (2015). The Evaluation of Factors Influencing Flights Delay at Czech International Airports. Acta Universitatis Agriculturae et Silviculturae Mendelianae Brunensis. 63. 2187-2196. 10.11118/201563062187.
  39. Zhu, B., Clarke, J.-P., & Zhu, J. (2016). Real-time integrated flight schedule recovery problem using sampling-based approach. Journal of Computational and Theoretical Nanoscience, 13(2), 1458-1467. https://doi.org/10.1166/jctn.2016.5068.
  40. Zhu, X., Lin, Y., He, Y., Tsui, K.-L., Chan, P. W., & Li, L. (2022). Short-term nationwide airport throughput prediction with graph attention recurrent neural network. Frontiers in Artificial Intelligence, 5, 884485. https://doi.org/10.3389/frai.2022.884485.
  41. Zou, B., & Hansen, M. (2012). Impact of operational performance on air carrier cost structure: Evidence from U.S. airlines. Transportation Research Part E: Logistics and Transportation Review, 48(5), 1032-1048. https://doi.org/10.1016/j.tre.2012.03.006.
  42. Pan, W., Wang, Z., Zhou, G., Pan, Z., & Zhang, H. (2015). Flight delay cost optimization based on ground power supply. In Proceedings of the 2015 International Industrial Informatics and Computer Engineering Conference. DOI 10.2991/iiicec-15.2015.77.